An Effective Scheduling-Based RFID Reader Collision Avoidance Model and Its Resource Allocation via Artificial Immune Network

Radio frequency identification, that is, RFID, is one of important technologies in Internet of Things. Reader collision does impair the tag identification efficiency of an RFID system. Many developed methods, for example, the scheduling-based series, that are used to avoid RFID reader collision, have been developed. For scheduling-based methods, communication resources, that is, time slots, channels, and power, are optimally assigned to readers. In this case, reader collision avoidance is equivalent to an optimization problem related to resource allocation. However, the existing methods neglect the overlap between the interrogation regions of readers, which reduces the tag identification rate (TIR). To resolve this shortage, this paper attempts to build a reader-to-reader collision avoidance model considering the interrogation region overlaps (R2RCAM-IRO). In addition, an artificial immune network for resource allocation (RA-IRO-aiNet) is designed to optimize the proposed model. For comparison, some comparative numerical simulations are arranged. The simulation results show that the proposed R2RCAM-IRO is an effective model where TIR is improved significantly. And especially in the application of reader-to-reader collision avoidance, the proposed RA-IRO-aiNet outperforms GA, opt-aiNet, and PSO in the total coverage area of readers.